Advanced Composite Materials

Kim, Myeong-Gi; Kim, Sang-Woo

2025 Advanced Composite Materials

doi: 10.1080/09243046.2024.2447142

This study investigates the impact of localization on a carbon fiber-reinforced polymer (CFRP) composite plate, considering the operating temperature of the composite structure. A multiplexed fiber Bragg grating (FBG) sensor, utilizing a cross-correlation-based impact localization algorithm, was employed for this purpose. This algorithm primarily depends on the shape of the impact response wavelength and is minimally affected by temperature variations. The study validates the effectiveness of the cross-correlation-based impact location detection technique with temperature compensation within the operational temperature range of composite structures. Results demonstrated that the algorithm successfully estimated the impact location for each of the 10 impact points at temperatures of −20, 0, 20, and 40 °C. Out of 40 impact points, 33 were accurately estimated under the given temperatures, with a total average error of 3.30 mm, which is significantly lower than the predefined grid size of 80 mm, equating to an error rate of 4.13%. Therefore, the cross-correlation-based impact localization algorithm has proven to be a promising tool for impact monitoring systems in composite structures under various temperature conditions.

Haruyama, Daichi; Kawagoe, Yoshiaki; Okabe, Tomonaga

2025 Advanced Composite Materials

doi: 10.1080/09243046.2024.2427460

An analytical model was developed to characterize the material behavior of ceramic matrix composites (CMCs). The model incorporates energy-based matrix crack growth, accounting for thermal residual and debonding stresses. Matrix crack growth was analyzed using the Monte Carlo method to represent the random nature of crack growth, which considers the distribution of defects within the material. Additionally, a periodic matrix crack growth model was examined. The results from the model, enhanced with correction factors, showed strong agreement with those obtained from Monte Carlo simulations. Furthermore, the global load sharing (GLS) model that includes the probability of matrix cracks propagating into the fibers was proposed and integrated with the periodic matrix crack growth model. This combined model accurately expressed the stress–strain and crack density–stress relationships up to the point of fiber breakage. The study also examined the propagation of matrix cracks into the fibers in relation to debonding stress and energy, leading to the successful validation of the proposed model.

Li, Hao; Pang, Leishuo; Wang, Wenchao; Yu, Tian; Zhang, Zijian; Li, Shipeng

2025 Advanced Composite Materials

doi: 10.1080/09243046.2024.2448081

Carbon Fiber Reinforced Thermoplastic Plastic (CFRTP) has been increasingly used in aerospace and automotive manufacturing with its excellent mechanical properties. Based on the melt-curing characteristics of thermoplastic composites, combining the full-thickness reinforced joining technology with induction welding can provide an effective way for high-strength joining. In this paper, the full factorial experimental design method is used to deeply explore the influence law of welding time, consolidation force and heating current on the tensile properties of welded joints. Combined with Sparrow Search Algorithm (SSA) and BP neural network, a welding joint tensile strength prediction model was constructed. In addition, a multi-objective model based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was developed. The multi-objective optimization method of TOPSIS was used to select the optimal parameter combinations with ultimate tensile strength and first debonding strength as the optimization objectives: welding time of 29.995 min, consolidation force of 638.669 N, and heating current of 443.351 A. Experimental studies have shown that the optimized welding joints have an increase in ultimate tensile strength of up to 32.4 % and an increase in first time debonding strength of up to 47.0 % with respect to the non-optimized welding joints.

Sahoo, Nityananda; Alam, Syed Nasimul; Ghosh, Arka; Sahoo, Kalpana; Das, Bappa; Kar, Uttam; Shrivastava, Pankaj; Rajoriya, Anuj

2025 Advanced Composite Materials

doi: 10.1080/09243046.2025.2450176

In the present study, Al-based hybrid nanocomposites were developed by powder metallurgy technique using binary hybrid nanofillers consisting of exfoliated MoS2 and GnP as nanofillers. The impact of the MoS2-GnP hybrid nanofiller on the microstructure, mechanical and tribological properties of the Al-based hybrid nanocomposites have been studied. Initially, bulk MoS2 was exfoliated by milling in a high energy planetary ball mill for 30 h and the GnP was synthesized by subjecting the graphite intercalation compound to a thermal shock and subsequently ultrasonicating the thermally exfoliated GnP. The effective exfoliation and structural refinement of both MoS2 and GnP have been confirmed by X-ray diffraction and scanning electron microscopy analysis. Exfoliated MoS2 and GnP were later blended in different ratios of their weight fraction by ultrasonication in an acetone medium. The Al matrix was then reinforced with the various binary hybrid nanofillers consisting of MoS2 and GnP. Wear analysis revealed mechanisms such as abrasion, adhesion, ploughing, delamination, microcracks, deep grooves, and nanofiller pullout in the case of all the nanocomposites. The Al-1 wt.% MoS2(0.3)GnP(0.7) nanocomposite shows superior properties, including the highest relative density (~93.15%), hardness (~476.28 MPa), compressive strength (~337.76 MPa) and outstanding wear resistance among all the Al-MoS2-GnP nanocomposites. Notably, it was observed that straying from the optimal reinforcement loading level can have a detrimental effect on the physical, mechanical, and wear properties of the nanocomposites, resulting in diminished performance and reduced material integrity. The significant improvement in the wear properties of the Al-1 wt.% MoS2(0.3)GnP(0.7) nanocomposite can be attributed to the self-lubricating properties of MoS2 and GnP.